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Record W3011776721 · doi:10.1038/s41598-020-61277-8

The hazardous 2017–2019 surge and river damming by Shispare Glacier, Karakoram

2020· article· en· W3011776721 on OpenAlexaff
Rakesh Bhambri, C. Scott Watson, Kenneth Hewitt, Umesh K. Haritashya, Jeffrey S. Kargel, Arjun Pratap Shahi, Pritam Chand, Amit Kumar, Akshaya Verma, Himanshu Govil

Bibliographic record

VenueScientific Reports · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsWilfrid Laurier University
FundersNatural Environment Research CouncilNational Aeronautics and Space AdministrationWadia Institute of Himalayan GeologySight Research UKNuclear Safety and Security CommissionUniversity of Dayton
KeywordsSurgeGlacierMeltwaterGeologyTributaryGlacier terminusTidewater glacier cycleGlacial periodGlacier ice accumulationChannelizedHydrology (agriculture)Physical geographyGeomorphologyIce streamOceanographyCryosphereIce calvingSea iceGeography

Abstract

fetched live from OpenAlex

Abstract In 2017–2019 a surge of Shispare Glacier, a former tributary of the once larger Hasanabad Glacier (Hunza region), dammed the proglacial river of Muchuhar Glacier, which formed an ice-dammed lake and generated a small Glacial Lake Outburst Flood (GLOF). Surge movement produced the highest recorded Karakoram glacier surface flow rate using feature tracking (~18 ± 0.5 m d −1 ) and resulted in a glacier frontal advance of 1495 ± 47 m. The surge speed was less than reports of earlier Hasanabad advances during 1892/93 (9.3 km) and 1903 (9.7 km). Surges also occurred in 1973 and 2000–2001. Recent surges and lake evolution are examined using feature tracking in satellite images (1990–2019), DEM differencing (1973–2019), and thermal satellite data (2000–2019). The recent active phase of Shispare surge began in April 2018, showed two surface flow maxima in June 2018 and May 2019, and terminated following a GLOF on 22–23 June 2019. The surge likely had hydrological controls influenced in winter by compromised subglacial flow and low meltwater production. It terminated during summer probably because increased meltwater restored efficient channelized flow. We also identify considerable heterogeneity of movement, including spring/summer accelerations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.207
Teacher spread0.187 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations101
Published2020
Admission routes1
Has abstractyes

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